傅斌贺 , 刘维平 , 聂俊峰 , 等 . 考虑认知行为差异的乘员信息作业绩效研究 [J ] . 兵工学报 , 2019 , 40 ( 3 ): 659 - 665 . DOI: 10.3969/j.issn.1000-1093.2019.03.026 http://doi.org/10.3969/j.issn.1000-1093.2019.03.026 认知行为差异逐渐成为乘员信息作业绩效的主要影响因素，为提高乘员的信息作业绩效，开展了考虑认知行为差异的装甲车辆乘员信息作业绩效研究。基于任务-网络建模技术，构建了融合通道维度的乘员信息作业认知行为模型，以及乘员信息作业绩效预测模型。针对不同认知能力水平，进行了乘员信息作业绩效对比实验；通过对绩效数据的统计分析，得到了乘员认知能力与作业绩效之间的相关关系和影响规律。结果表明：乘员信息作业绩效预测结果与实验数据具有较好的一致性；乘员认知能力与信息作业绩效呈显著的正相关关系，乘员认知行为差异对信息作业绩效具有显著影响。建议装甲车辆乘员选拔应结合认知能力测评，并进行针对性训练和任务分工，可以提高乘员作业绩效，节约训练资源。

FU B H , LIU W P , NIE J F , et al. Research on crew’s information operation performance with the difference of cognitive behavior [J ] . Acta Armamentarii , 2019 , 40 ( 3 ): 659 - 665 . (in Chinese)

ZHOU Y Y , HUANG S , XU Z M , et al. Cognitive workload recognition using EEG signals and machine learning: a review [J ] . IEEE Transactions on Cognitive and Developmental Systems , 2022 , 14 ( 3 ): 799 - 818 .

SOLÍS-MARCOS I , KIRCHER K . Event-related potentials as indices of mental workload while using an in-vehicle information system [J ] . Cognition, Technology & Work , 2019 , 21 ( 1 ): 55 - 67 .

GHANI U , SIGNAL N , NIAZI I K , et al. A novel approach to validate the efficacy of single task ERP paradigms to measure cognitive workload [J ] . International Journal of Psychophysiology , 2020 , 158 : 9 - 15 . DOI: 10.1016/j.ijpsycho.2020.09.007 http://doi.org/10.1016/j.ijpsycho.2020.09.007 The present study examined the utility of a single-task paradigm to evaluate cognitive workload. The cognitive workload from twenty-five healthy participants was measured during a tilt-ball game while tones were presented in the background to generate event-related potentials (ERPs) in electroencephalographic (EEG) data. In the game, participants were instructed to move the ball to highlighted targets and avoid moving obstacles. The game's difficulty level was manipulated (easy, medium, hard) by adjusting the number and speed of the moving obstacles. The difficulty levels were presented in a random order during multiple short runs to minimize the effects of habituation, fatigue, and boredom. The behavioral results showed that greater task difficulty resulted in a significant decrease (p < 0.001) in game performance, i.e., participants achieved few targets with a high collision rate. To evaluate cognitive workload, we measured the amplitude of early ERP components (N1, P1, and P2) corresponding to the involuntary attention orienting response. The amplitude of the N1 component decreased significantly (p = 0.029) with an increase in cognitive workload. These findings suggest that the early ERP component, specifically the N1, corresponds to attention orienting response, and that the task difficulty modulates it. This study provided evidence that the inverse relationship between ERP components and cognitive workload can be reliably assessed by controlling for other factors such as habituation or boredom during a single task paradigm.Copyright © 2020 Elsevier B.V. All rights reserved.

CAUSSE M , FABRE E , GIRAUDET L , et al. EEG/ERP as a measure of mental workload in a simple piloting task [J ] . Procedia Manufacturing , 2015 , 3 : 5230 - 5236 .

DIAZ-PIEDRA C , SEBASTIÁN M V , DI STASI L L. EEG theta power activity reflects workload among army combat drivers: an experimental study [J ] . Brain Sciences , 2020 , 10 ( 4 ): 199 .

WANG Q , SMYTHE D , CAO J , et al. Characterisation of cognitive load using machine learning classifiers of electroencephalogram data [J ] . Sensors , 2023 , 23 ( 20 ): 8528 .

PLECHAWSKA-WÓJCIK M , TOKOVAROV M , KACZOROWSKA M , et al. A three-class classification of cognitive workload based on EEG spectral data [J ] . Applied Sciences , 2019 , 9 ( 24 ): 5340 .

GORJI H T , WILSON N , VANBREE J , et al. Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight [J ] . Scientific Reports , 2023 , 13 ( 1 ): 2507 . DOI: 10.1038/s41598-023-29647-0 http://doi.org/10.1038/s41598-023-29647-0 Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.© 2023. The Author(s).

GUAN K , ZHANG Z M , CHAI X K , et al. EEG based dynamic functional connectivity analysis in mental workload tasks with different types of information [J ] . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2022 , 30 : 632 - 642 . DOI: 10.1109/TNSRE.2022.3156546 http://doi.org/10.1109/TNSRE.2022.3156546 The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information.

DIMITRAKOPOULOS G N , KAKKOS I , DAI Z X , et al. Task-independent mental workload classification based upon common multiband EEG cortical connectivity [J ] . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2017 , 25 ( 11 ): 1940 - 1949 . DOI: 10.1109/TNSRE.2017.2701002 http://doi.org/10.1109/TNSRE.2017.2701002 Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.

毛明 , 刘勇 , 胡建军 . 坦克装甲车辆综合电子信息系统的总体设计研究 [J ] . 兵工学报 , 2017 , 38 ( 6 ): 1192 - 1202 . DOI: 10.3969/j.issn.1000-1093.2017.06.020 http://doi.org/10.3969/j.issn.1000-1093.2017.06.020 车辆综合电子信息系统（简称车电）是坦克装甲车辆平台信息化的基础和核心，直接影响坦克装甲车辆信息化水平和战术性能。由于对其兼具信息化总体和车电功能系统双重角色的内涵认识不清晰，车电发展受到一定制约。阐明了车电系统本质内涵，得出“坦克装甲车辆综合电子信息系统的关键在于综合”的结论，并通过梳理、分析车电技术的发展进程，凝炼了传感综合、显控综合、席位综合、处理综合与信息综合等重点，提出车电系统的关键技术、总体设计流程及仿真建模方法，为坦克装甲车辆综合电子信息系统发展提供借鉴。

MAO M , LIU Y , HU J J . Research on the overall design of integrated electronic information system for tanks and armored vehicles [J ] . Acta Armamentarii , 2017 , 38 ( 6 ): 1192 - 1202 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2017.06.020 http://doi.org/10.3969/j.issn.1000-1093.2017.06.020 Integrated electronic information system, which is the basis and core to fulfill the informatization of tanks and armed vehicles, has direct impacts on the platform informatization level and tactical capabilities. The development of integrated electronic information system has been constrained to some extent due to lack of clear understanding on its double role as general informatization unit and vetronics function system. The essential meaning of vetronics system is illustrated, and it is concluded that the electronic information system of tanks and armored vehicles relies on system integration. The history and development process of vetronics technology are reviewed and analyzed, and the important technique points of sensor integration, display and control integration, seat role integration, processing and information integration are discussed and summarized. The key technology, as well as the general design process and modeling simulation method of vetronics system are proposed, which could be considered to provide some useful directions for the evolution of the integrated electronic information system of tanks and armored vehicles. Key

MA Z , ZHANG Y Q . Driver-automated vehicle interaction in mixed traffic: types of interaction and drivers’ driving styles [J ] . Human Factors: The Journal of the Human Factors and Ergonomics Society , 2024 , 66 ( 2 ): 544 - 561 .

DETJEN H , FALTAOUS S , PFLEGING B , et al. How to increase automated vehicles’ acceptance through In-vehicle interaction design: a review [J ] . International Journal of Human-Computer Interaction , 2021 , 37 ( 4 ): 308 - 330 .

LOEW A , KONIAKOWSKY I , FORSTER Y , et al. The impact of speech-based assistants on the driver’s cognitive distraction [J ] . Accident Analysis & Prevention , 2023 , 179 : 106898 .

MAO M , XIE F , HU J J , et al. Analysis of workload of tank crew under the conditions of informatization [J ] . Defence Technology , 2014 , 10 ( 1 ): 17 - 21 . DOI: 10.1016/j.dt.2013.12.008 http://doi.org/10.1016/j.dt.2013.12.008 A consensus has been reached that the tanks need to be integrated into the informatization battlefield. With the development of technology, the tank crew has being gradually decreased, so the research on two-soldier crew tank has become a hotspot. The workload of tank crew under the conditions of informatization is analyzed based on the combat mission of tank and the typical combat scenarios, and the impact of new technologies on workload is evaluated. The crew members in tank can be reduced from three to two, but it is necessary to substantially improve the automation of target search and the reliability of each subsystem and component. © 2014 China Ordnance Society

WICKENS C D . Multiple resources and mental workload [J ] . Human Factors: The Journal of the Human Factors and Ergonomics Society , 2008 , 50 ( 3 ): 449 - 455 .

孙晓东 , 金晓萍 , 解芳 , 等 . 多模态告警和认知负荷对装甲车辆乘员反应的影响 [J ] . 兵工学报 , 2023 , 44 ( 4 ): 972 - 981 .

SUN X D , JIN X P , XIE F , et al. Effects of multimodal warning and cognitive load on the response of armored vehicle occupants [J ] . Acta Armamentarii , 2023 , 44 ( 4 ): 972 - 981 . (in Chinese) DOI: 10.12382/bgxb.2022.0018 http://doi.org/10.12382/bgxb.2022.0018 To explore the effects of multimodal warning on the response of armored vehicle occupants regarding the direction of enemy vehicles under different cognitive load levels, 20 adult males were recruited to take two-factor ergonomic experiments on the warning type and cognitive load level. The experiments were based on a virtual simulated armored vehicle occupant task platform designed for a hypothetical operational task for armored vehicle occupants and warned the occupants under different cognitive load levels about enemy vehicle directions. Results show that multimodal warning with haptics was easier for occupants to understand the situation and more in line with their cognitive state than visual warning. This type of warning significantly reduced their reaction time, annihilation time, and response error rate. Occupants with a high cognitive load level showed a significant decrease in information comprehension and a significant increase in reaction time, annihilation time, and response error rate compared to those with a lower load. The findings suggest that multimodal warning can be used to speed up occupant reaction and improve combat performance when the visual channel is overloaded or the cognitive load is high. This study provides a theoretical basis for designing multimodal human-computer interaction warnings in armored vehicle compartments.

DOUDOU M , BOUABDALLAH A , BERGE-CHERFAOUI V . Driver drowsiness measurement technologies: current research, market solutions, and challenges [J ] . International Journal of Intelligent Transportation Systems Research , 2020 , 18 ( 2 ): 297 - 319 .

JIANG Z B , LI X Y , GE L Z , et al. Using multimodal methods and machine learning to recognize mental workload: distinguishing between underload, moderate load, and overload [J ] . International Journal of Human-Computer Interaction , 2024 , 41 ( 8 ): 1 - 17 .

ZARJAM P , EPPS J , CHEN F . Characterizing working memory load using EEG delta activity [C ] // Proceedings of the 2011 19th European Signal Processing Conference. Barcelona, Spain:IEEE , 2011 : 1554 - 1558 .

KUTAFINA E , HEILIGERS A , POPOVIC R , et al. Tracking of mental workload with a mobile EEG sensor [J ] . Sensors , 2021 , 21 ( 15 ): 5205 .

FAVRETTO M A , RETTORE ANDREIS F , COSSUL S , et al. Motor unit behaviour at high force levels in diabetic patients with peripheral neuropathy [C ] // Proceedings of the IX Latin American Congress on Biomedical Engineering and the XXVIII Brazilian Congress on Engineering Biomedical Engineering. Florianópolis , Brazil : Galoá Proceedings , 2024 .

NIEDERMEYER E . Electrophysiology of the frontal lobe [J ] . Clinical Electroencephalography , 2003 , 34 ( 1 ): 5 - 12 .

ZHOZHIKASHVILI N , ZAKHAROV I , ISMATULLINA V , et al. Parietal alpha oscillations: cognitive load and mental toughness [J ] . Brain Sciences , 2022 , 12 ( 9 ): 1135 .

BEHRMANN M , GENG J J , SHOMSTEIN S . Parietal cortex and attention [J ] . Current Opinion in Neurobiology , 2004 , 14 ( 2 ): 212 - 217 . DOI: 10.1016/j.conb.2004.03.012 http://doi.org/10.1016/j.conb.2004.03.012 The parietal lobe forms about 20% of the human cerebral cortex and is divided into two major regions, the somatosensory cortex and the posterior parietal cortex. Posterior parietal cortex, located at the junction of multiple sensory regions, projects to several cortical and subcortical areas and is engaged in a host of cognitive operations. One such operation is selective attention, the process where by the input is filtered and a subset of the information is selected for preferential processing. Recent neuroimaging and neuropsychological studies have provided a more fine-grained understanding of the relationship between brain and behavior in the domain of selective attention.

CHIKHI S , MATTON N , BLANCHET S . EEG power spectral measures of cognitive workload: a meta-analysis [J ] . Psychophysiology , 2022 , 59 ( 6 ): e14009 .

DING Y , CAO Y Q , DUFFY V G , et al. Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning [J ] . Ergonomics , 2020 , 63 ( 7 ): 896 - 908 . DOI: 10.1080/00140139.2020.1759699 http://doi.org/10.1080/00140139.2020.1759699 This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.

VAN TULDER G , DE BRUIJNE M . Learning cross-modality representations from multi-modal images [J ] . IEEE Transactions on Medical Imaging , 2018 , 38 ( 2 ): 638 - 648 .

FONSECA A , KERICK S , KING J T , et al. Brain network changes in fatigued drivers: a longitudinal study in a real-world environment based on the effective connectivity analysis and actigraphy data [J ] . Frontiers in Human Neuroscience , 2018 , 12 : 418 . DOI: 10.3389/fnhum.2018.00418 http://doi.org/10.3389/fnhum.2018.00418 The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.

JACKSON A F , BOLGER D J . The neurophysiological bases of EEG and EEG measurement: a review for the rest of us [J ] . Psychophysiology , 2014 , 51 ( 11 ): 1061 - 1071 . DOI: 10.1111/psyp.12283 http://doi.org/10.1111/psyp.12283 A thorough understanding of the EEG signal and its measurement is necessary to produce high quality data and to draw accurate conclusions from those data. However, publications that discuss relevant topics are written for divergent audiences with specific levels of expertise: explanations are either at an abstract level that leaves readers with a fuzzy understanding of the electrophysiology involved, or are at a technical level that requires mastery of the relevant physics to understand. A clear, comprehensive review of the origin and measurement of EEG that bridges these high and low levels of explanation fills a critical gap in the literature and is necessary for promoting better research practices and peer review. The present paper addresses the neurophysiological source of EEG, propagation of the EEG signal, technical aspects of EEG measurement, and implications for interpretation of EEG data. Copyright © 2014 Society for Psychophysiological Research.

JUSTESEN A B , FOGED M T , FABRICIUS M , et al. Diagnostic yield of high-density versus low-density EEG: the effect of spatial sampling, timing and duration of recording [J ] . Clinical Neurophysiology , 2019 , 130 ( 11 ): 2060 - 2064 . DOI: S1388-2457(19)31193-9 http://doi.org/S1388-2457(19)31193-9 To investigate the effect of spatial sampling and of recording duration on the diagnostic yield of EEG for identification of interictal epileptiform discharges (IEDs). Previous studies demonstrated that high-density (HD) recordings increased accuracy of localization compared to low-density (LD) recordings.We have prospectively evaluated the effect of spatial sampling and of recording duration in patients who had short-term (ST) recordings with a HD array of 256 electrodes following long-term (LT) recordings with a LD array consisting of the standard IFCN array of 25 electrodes. IED clusters were identified in four datasets: LT-LD, ST-LD (spatially down-sampled to the standard IFCN array), ST-HD and a shortened (90 minutes) epoch of LT-LD.Sixty consecutive patients were recruited. We identified 89 IED clusters totally. Two clusters were found by increasing spatial sampling from 25 to 256 electrodes. This modest increase was not statistically significant. Eight clusters were missed by reducing the recording duration to 90 minutes, as compared with the LT recordings (p = 0.003).Recording duration is more important for the diagnostic yield of EEGs than increasing spatial sampling beyond the standard IFCN electrode array.The standard IFCN electrode array provides sufficient spatial sampling for identification of the IEDs.Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.